Conspiracy Theorizing Surveillance: considering modalities of paranoia and conspiracy in surveillance studies
Bibliographic record
Abstract
In this paper I argue that the notion of paranoia can inform a post-panoptic theory of surveillance, without simply functioning as a pre-emptive dismissal of a critical engagement with technologies and regimes of surveillance as just paranoid. Rather, I seek to address how paranoia can be rearticulated to serve a productive, non-pathological function in an analysis of logics of surveillance. To this end, I consider the manner in which paranoia is characterised in popular cultural narratives and how the advent of cultural paranoia can be understood in the context of the expansion of state and corporate surveillance, especially in the UK and post-9/11 North America. Drawing on this notion of cultural paranoia, I then argue for three modalities of paranoia-as-surveillance theory. The first modality, the paranoia of the subject of surveillance, addresses the divergent panoptic subject who rejects the disciplinary logic of the panopticon; the second modality considers how the paranoid as the suspicious subject could be used to characterise the expansion of surveillance regimes through an ever-present need to observe; and the third modality of conspiracy theory proposes that a paranoid logic, akin to that of the conspiracy theory, sutures over epistemic gaps in the interpretation of information in instances of analytic deficit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.094 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".